---
title: Visual AI predictions
description: There are a variety of methods for making predictions from DataRobot's image models; use Base64 encoding and sample scripts to simplify.
---

# Visual AI predictions {: #visual-ai-predictions }

There are various methods for making predictions from image models:

Method | Description | See...
------ | ----------- | -----
**UI predictions** | Use the same dataset format as the one used to create the project (upload a ZIP archive with one or more images). | [Make Predictions tab](predict)
**Model Deployment: API** <br> (real-time, small datasets)|  Use the base64 format (described below). | <ul><li>[Deploy tab (UI)](deploy-model)</li><li>[Prediction API](dr-predapi)</li></ul>
**Model Deployment: Batch**  <br> (large datasets)| For the API Client and HTTP Interface options, use the same dataset format as the original dataset used to create the project (i.e., upload a ZIP archive with one or more images). For the CLI Interface option use the base64 format (described below). | [Batch prediction scripts](cli-scripts)
**Portable Prediction Server**  | Use the base64 format (described below). | [Portable Prediction Server](portable-pps)

## Base64 encoding format {: #base64-encoding-format }

If your training dataset consists of a ZIP archive with one or more image files, the prediction dataset needs to be converted to a different format so that it is fully contained in a single CSV file.

## Sample scripts {: #sample-scripts }
See the links below for help with visual data conversion:

- Tutorial: [Getting Predictions for Visual AI Projects via API Calls](vai-pred)
- DataRobot Python package: <a target="_blank" href="https://datarobot-public-api-client.readthedocs-hosted.com/page/reference/modeling/spec/binary_data.html#preparing-data-for-predictions">Preparing data for predictions using the DataRobot library</a>
- Script: <a target="_blank" href="https://github.com/datarobot-community/visual-ai-data-prep">Comprehensive data prep script</a>

!!! note
    {% include 'includes/github-sign-in-plural.md' %}

The following shows sample usage:

`python visualai_data_prep.py pred_dataset.zip pred_dataset.csv image`

Where:

 * `visualai_data_prep.py` is the comprehensive Data Prep script used for making conversions to base64 format.
 * `pred_dataset.zip` is the input dataset (ZIP of images).
 * `pred_dataset.csv` is the output, which can be used via prediction API.
 * `image` is an image column name.


## Deep dive {: #deep-dive }
To convert a set of image files into a single CSV file, each image must be converted to <a target="_blank" href="https://en.wikipedia.org/wiki/Base64">base64 text</a>. This format allows DataRobot to embed images as a regular text column in the CSV. Encoding binary image data into base64 is a simple operation, present in all programming languages.

Here is an example in Python:

```python
import base64
import pandas as pd
from io import BytesIO
from PIL import Image


def image_to_base64(image: Image) -> str:
    img_bytes = BytesIO()
    image.save(img_bytes, 'jpeg', quality=90)
    image_base64 = base64.b64encode(img_bytes.getvalue()).decode('utf-8')
    return image_base64


# let's build a CSV with a single row that contains an image
# the same general approach works if you have multiple image rows or columns
image = Image.open('cat.jpg')
image_base64 = image_to_base64(image)

df = pd.DataFrame({'animal_image': [image_base64]})
df.to_csv('prediction_dataset.csv' index=False)
print(df)
```

!!! note
    Encode a binary image file (not decoded pixel contents) to base64. This example uses `PIL.Image` to open the file, but you can base64-encode an image file directly.